Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0

Abstract

Abstract. Modelling the risk of natural hazards for society, ecosystems, and the economy is subject to strong uncertainties, even more so in the context of a changing climate, evolving societies, growing economies, and declining ecosystems. Here, we present a new feature of the climate-risk modelling platform CLIMADA (CLIMate ADAptation), which allows us to carry out global uncertainty and sensitivity analysis. CLIMADA underpins the Economics of Climate Adaptation (ECA) methodology which provides decision-makers with a fact base to understand the impact of weather and climate on their economies, communities, and ecosystems, including the appraisal of bespoke adaptation options today and in future. We apply the new feature to an ECA analysis of risk from tropical cyclone storm surge to people in Vietnam to showcase the comprehensive treatment of uncertainty and sensitivity of the model outputs, such as the spatial distribution of risk exceedance probabilities or the benefits of different adaptation options. We argue that broader application of uncertainty and sensitivity analysis will enhance transparency and intercomparison of studies among climate-risk modellers and help focus future research. For decision-makers and other users of climate-risk modelling, uncertainty and sensitivity analysis has the potential to lead to better-informed decisions on climate adaptation. Beyond provision of uncertainty quantification, the presented approach does contextualize risk assessment and options appraisal, and might be used to inform the development of storylines and climate adaptation narratives.

Projections and uncertainties of future winter windstorm damage in Europe

“…The uncertainty and sensitivity analysis carried out in this study is done entirely with CLIMADA's unsequa module and follows the essential steps described in Kropf et al (2022). We first define input variables and input parameters that represent relevant factors of uncertainty in the modelling of future winter storm damage over Europe.…”

“…Uncertainty in the modelling of the exposure is accounted for by varying the m and n exponents of the LitPop exposure data, which respectively govern the weight given to the population count and nightlight intensity data layers used for the spatial disaggregation. Varying the m and n exponents allows us to simulate uncertainty in the geographical distribution of the physical assets, with higher values of n emphasizing highly populated areas, and lower values of n less densely populated areas (Kropf et al, 2022). According to Eberenz et al (2020), m = 1, and n = 1 is the best performing parameterization for total value distribution in space.…”

“…Understanding and quantifying those uncertainties, and their influence on the outcome of the risk projection is crucial for risk assessment studies. Uncertainty and sensitivity analyses are a commonly used method to study and model uncertainties associated with the use of complex models such as climate-risk models (Saltelli et al, 2008;Pianosi et al, 2016;Kropf et al, 2022). Uncertainty and sensitivity analyses rely on the generation of many replications of a model's output, where the replications are obtained by varying some of the model's input factors which are considered as uncertain.…”

A generalized framework for designing open-source natural hazard parametric insurance

“…This renders a coarse representation of vulnerability, which might deviate considerably from the vulnerability of hospitals to these hazards. Also in traditional risk estimates the vulnerability constitutes a major uncertainty (Kropf et al, 2022). Besides quantifying related uncertainties (Kropf et al, 2022), users might choose to apply conservative impact functions (e.g.…”

Unraveling unknowns of future tropical cyclone risks

“…We use the uncertainty and sensitivity quantification (unsequa) module of CLIMADA 12 to compute the model uncertainties and sensitivity indices reported in this study. This module seamlessly integrates the SALib -Sensitivity Analysis Library in Python package 55 into the CLIMADA risk model, hence supporting all sampling and sensitivity index algorithms implemented therein.…”